An image generated by an infrared imager, such as, for example, a microbolometer-based infrared imager, typically includes noise. For example, the dominant source of noise may be due to temporal, 1/f, and/or fixed spatial noise, and a typical infrared imaging system may include calibration algorithms to try to minimize these types of noise.
As an example, an infrared imaging system generally uses an internal shutter that lets the infrared imaging system acquire an image against a uniform target to perform calibration procedures. However, there are some drawbacks associated with this type of procedure. For example, the requirement of a shutter increases manufacturing costs of the infrared imaging system. In addition, the calibration procedure does not correct for noise sources beyond the shutter (e.g., due to a lens or other components within the optical path). Also, the scene temperature may be different from that of the shutter and the detector elements response to irradiation may not be completely linear. As such, a correction made at the shutter temperature may not be appropriate for the particular scene that the infrared imaging system is imaging. Moreover, during offset calibration using the shutter, the infrared imaging system may not be available to capture images of a scene, as desired by a user.
Human observers are particularly sensitive to high frequency spatial noise that is typical of infrared imagers. Low pass filters can be used to reduce noise but this blurs the image and lowers the system performance.
As a result, there is a need for improved techniques directed to infrared imaging and processing of infrared images that may reduce spatial, temporal, and/or 1/f type noise without relying on a shutter or requiring a uniform scene and that may introduce no or minimal blur.